__init__.py

#! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# graph_tool -- a general graph manipulation python module
#
# Copyright (C) 2006-2013 Tiago de Paula Peixoto <tiago@skewed.de>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.s

"""
``graph_tool.generation`` - Random graph generation
---------------------------------------------------

Summary
+++++++

.. autosummary::
   :nosignatures:

   random_graph
   random_rewire
   predecessor_tree
   line_graph
   graph_union
   triangulation
   lattice
   geometric_graph
   price_network
   complete_graph
   circular_graph

Contents
++++++++
"""

from __future__ import division, absolute_import, print_function

from .. dl_import import dl_import
dl_import("from . import libgraph_tool_generation")

from .. import Graph, GraphView, _check_prop_scalar, _prop, _limit_args, _gt_type, _get_rng
from .. stats import label_parallel_edges, label_self_loops
import inspect
import types
import sys, numpy, numpy.random

__all__ = ["random_graph", "random_rewire", "predecessor_tree", "line_graph",
           "graph_union", "triangulation", "lattice", "geometric_graph",
           "price_network", "complete_graph", "circular_graph"]


def random_graph(N, deg_sampler, directed=True,
                 parallel_edges=False, self_loops=False, block_membership=None,
                 block_type="int", degree_block=False,
                 random=True, verbose=False, **kwargs):
    r"""
    Generate a random graph, with a given degree distribution and (optionally)
    vertex-vertex correlation.

    The graph will be randomized via the :func:`~graph_tool.generation.random_rewire`
    function, and any remaining parameters will be passed to that function.
    Please read its documentation for all the options regarding the different
    statistical models which can be chosen.

    Parameters
    ----------
    N : int
        Number of vertices in the graph.
    deg_sampler : function
        A degree sampler function which is called without arguments, and returns
        a tuple of ints representing the in and out-degree of a given vertex (or
        a single int for undirected graphs, representing the out-degree). This
        function is called once per vertex, but may be called more times, if the
        degree sequence cannot be used to build a graph.

        Optionally, you can also pass a function which receives one or two
        arguments. If ``block_membership == None``, the single argument passed
        will be the index of the vertex which will receive the degree.  If
        ``block_membership != None``, the first value passed will be the vertex
        index, and the second will be the block value of the vertex.
    directed : bool (optional, default: ``True``)
        Whether the generated graph should be directed.
    parallel_edges : bool (optional, default: ``False``)
        If ``True``, parallel edges are allowed.
    self_loops : bool (optional, default: ``False``)
        If ``True``, self-loops are allowed.
    block_membership : list or :class:`~numpy.ndarray` or function (optional, default: ``None``)
        If supplied, the graph will be sampled from a stochastic blockmodel
        ensemble, and this parameter specifies the block membership of the
        vertices, which will be passed to the
        :func:`~graph_tool.generation.random_rewire` function.

        If the value is a list or a :class:`~numpy.ndarray`, it must have
        ``len(block_membership) == N``, and the values will define to which
        block each vertex belongs.

        If this value is a function, it will be used to sample the block
        types. It must be callable either with no arguments or with a single
        argument which will be the vertex index. In either case it must return
        a type compatible with the ``block_type`` parameter.
    block_type : string (optional, default: ``"int"``)
        Value type of block labels. Valid only if ``block_membership != None``.
    degree_block : bool (optional, default: ``False``)
        If ``True``, the degree of each vertex will be appended to block labels
        when constructing the blockmodel, such that the resulting block type
        will be a pair :math:`(r, k)`, where :math:`r` is the original block
        label.
    random : bool (optional, default: ``True``)
        If ``True``, the returned graph is randomized. Otherwise a deterministic
        placement of the edges will be used.
    verbose : bool (optional, default: ``False``)
        If ``True``, verbose information is displayed.

    Returns
    -------
    random_graph : :class:`~graph_tool.Graph`
        The generated graph.
    blocks : :class:`~graph_tool.PropertyMap`
        A vertex property map with the block values. This is only returned if
        ``block_membership != None``.

    See Also
    --------
    random_rewire: in-place graph shuffling

    Notes
    -----
    The algorithm makes sure the degree sequence is graphical (i.e. realizable)
    and keeps re-sampling the degrees if is not. With a valid degree sequence,
    the edges are placed deterministically, and later the graph is shuffled with
    the :func:`~graph_tool.generation.random_rewire` function, with all
    remaining parameters passed to it.

    The complexity is :math:`O(V + E)` if parallel edges are allowed, and
    :math:`O(V + E \times\text{n-iter})` if parallel edges are not allowed.


    .. note ::

        If ``parallel_edges == False`` this algorithm only guarantees that the
        returned graph will be a random sample from the desired ensemble if
        ``n_iter`` is sufficiently large. The algorithm implements an
        efficient Markov chain based on edge swaps, with a mixing time which
        depends on the degree distribution and correlations desired. If degree
        correlations are provided, the mixing time tends to be larger.

    Examples
    --------

    .. testcode::
       :hide:

       from numpy.random import randint, random, seed, poisson
       from pylab import *
       seed(43)
       gt.seed_rng(42)

    This is a degree sampler which uses rejection sampling to sample from the
    distribution :math:`P(k)\propto 1/k`, up to a maximum.

    >>> def sample_k(max):
    ...     accept = False
    ...     while not accept:
    ...         k = randint(1,max+1)
    ...         accept = random() < 1.0/k
    ...     return k
    ...

    The following generates a random undirected graph with degree distribution
    :math:`P(k)\propto 1/k` (with k_max=40) and an *assortative* degree
    correlation of the form:

    .. math::

        P(i,k) \propto \frac{1}{1+|i-k|}

    >>> g = gt.random_graph(1000, lambda: sample_k(40), model="probabilistic",
    ...                     vertex_corr=lambda i, k: 1.0 / (1 + abs(i - k)), directed=False,
    ...                     n_iter=100)
    >>> gt.scalar_assortativity(g, "out")
    (0.6285094791115295, 0.010745128857935755)

    The following samples an in,out-degree pair from the joint distribution:

    .. math::

        p(j,k) = \frac{1}{2}\frac{e^{-m_1}m_1^j}{j!}\frac{e^{-m_1}m_1^k}{k!} +
                 \frac{1}{2}\frac{e^{-m_2}m_2^j}{j!}\frac{e^{-m_2}m_2^k}{k!}

    with :math:`m_1 = 4` and :math:`m_2 = 20`.

    >>> def deg_sample():
    ...    if random() > 0.5:
    ...        return poisson(4), poisson(4)
    ...    else:
    ...        return poisson(20), poisson(20)
    ...

    The following generates a random directed graph with this distribution, and
    plots the combined degree correlation.

    >>> g = gt.random_graph(20000, deg_sample)
    >>>
    >>> hist = gt.combined_corr_hist(g, "in", "out")
    >>>
    >>> clf()
    >>> imshow(hist[0].T, interpolation="nearest", origin="lower")
    <...>
    >>> colorbar()
    <...>
    >>> xlabel("in-degree")
    <...>
    >>> ylabel("out-degree")
    <...>
    >>> savefig("combined-deg-hist.pdf")

    .. testcode::
       :hide:

       savefig("combined-deg-hist.png")

    .. figure:: combined-deg-hist.*
        :align: center

        Combined degree histogram.

    A correlated directed graph can be build as follows. Consider the following
    degree correlation:

    .. math::

         P(j',k'|j,k)=\frac{e^{-k}k^{j'}}{j'!}
         \frac{e^{-(20-j)}(20-j)^{k'}}{k'!}

    i.e., the in->out correlation is "disassortative", the out->in correlation
    is "assortative", and everything else is uncorrelated.
    We will use a flat degree distribution in the range [1,20).

    >>> p = scipy.stats.poisson
    >>> g = gt.random_graph(20000, lambda: (sample_k(19), sample_k(19)),
    ...                     model="probabilistic",
    ...                     vertex_corr=lambda a,b: (p.pmf(a[0], b[1]) *
    ...                                              p.pmf(a[1], 20 - b[0])),
    ...                     n_iter=100)

    Lets plot the average degree correlations to check.

    >>> clf()
    >>> axes([0.1,0.15,0.63,0.8])
    <...>
    >>> corr = gt.avg_neighbour_corr(g, "in", "in")
    >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-",
    ...         label=r"$\left<\text{in}\right>$ vs in")
    <...>
    >>> corr = gt.avg_neighbour_corr(g, "in", "out")
    >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-",
    ...         label=r"$\left<\text{out}\right>$ vs in")
    <...>
    >>> corr = gt.avg_neighbour_corr(g, "out", "in")
    >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-",
    ...          label=r"$\left<\text{in}\right>$ vs out")
    <...>
    >>> corr = gt.avg_neighbour_corr(g, "out", "out")
    >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-",
    ...          label=r"$\left<\text{out}\right>$ vs out")
    <...>
    >>> legend(bbox_to_anchor=(1.01, 0.5), loc="center left", borderaxespad=0.)
    <...>
    >>> xlabel("Source degree")
    <...>
    >>> ylabel("Average target degree")
    <...>
    >>> savefig("deg-corr-dir.pdf")

    .. testcode::
       :hide:

       savefig("deg-corr-dir.png")

    .. figure:: deg-corr-dir.*
        :align: center

        Average nearest neighbour correlations.


    **Stochastic blockmodels**


    The following example shows how a stochastic blockmodel
    [holland-stochastic-1983]_ [karrer-stochastic-2011]_ can be generated. We
    will consider a system of 10 blocks, which form communities. The connection
    probability will be given by

    >>> def corr(a, b):
    ...    if a == b:
    ...        return 0.999
    ...    else:
    ...        return 0.001

    The blockmodel can be generated as follows.

    >>> g, bm = gt.random_graph(5000, lambda: poisson(10), directed=False,
    ...                         model="blockmodel-traditional",
    ...                         block_membership=lambda: randint(10),
    ...                         vertex_corr=corr)
    >>> gt.graph_draw(g, vertex_fill_color=bm, edge_color="black", output="blockmodel.pdf")
    <...>

    .. testcode::
       :hide:

       gt.graph_draw(g, vertex_fill_color=bm, edge_color="black", output="blockmodel.png")

    .. figure:: blockmodel.*
        :align: center

        Simple blockmodel with 10 blocks.


    References
    ----------
    .. [metropolis-equations-1953]  Metropolis, N.; Rosenbluth, A.W.;
       Rosenbluth, M.N.; Teller, A.H.; Teller, E. "Equations of State
       Calculations by Fast Computing Machines". Journal of Chemical Physics 21
       (6): 1087-1092 (1953). :doi:`10.1063/1.1699114`
    .. [hastings-monte-carlo-1970] Hastings, W.K. "Monte Carlo Sampling Methods
       Using Markov Chains and Their Applications". Biometrika 57 (1): 97-109 (1970).
       :doi:`10.1093/biomet/57.1.97`
    .. [holland-stochastic-1983] Paul W. Holland, Kathryn Blackmond Laskey, and
       Samuel Leinhardt, "Stochastic blockmodels: First steps," Social Networks
       5, no. 2: 109-13 (1983) :doi:`10.1016/0378-8733(83)90021-7`
    .. [karrer-stochastic-2011] Brian Karrer and M. E. J. Newman, "Stochastic
       blockmodels and community structure in networks," Physical Review E 83,
       no. 1: 016107 (2011) :doi:`10.1103/PhysRevE.83.016107` :arxiv:`1008.3926`
    """

    g = Graph()

    if (type(block_membership) is types.FunctionType or
        type(block_membership) is types.LambdaType):
        btype = block_type
        bm = []
        if len(inspect.getargspec(block_membership)[0]) == 0:
            for i in range(N):
                bm.append(block_membership())
        else:
            for i in range(N):
                bm.append(block_membership(i))
        block_membership = bm
    elif block_membership is not None:
        btype = _gt_type(block_membership[0])

    if len(inspect.getargspec(deg_sampler)[0]) > 0:
        if block_membership is not None:
            sampler = lambda i: deg_sampler(i, block_membership[i])
        else:
            sampler = deg_sampler
    else:
        sampler = lambda i: deg_sampler()

    libgraph_tool_generation.gen_graph(g._Graph__graph, N, sampler,
                                       not parallel_edges,
                                       not self_loops, not directed,
                                       _get_rng(), verbose, True)
    g.set_directed(directed)

    if degree_block:
        if btype in ["object", "string"] or "vector" in btype:
            btype = "object"
        elif btype in ["int", "int32_t", "bool"]:
            btype = "vector<int32_t>"
        elif btype in ["long", "int64_t"]:
            btype = "vector<int64_t>"
        elif btype in ["double"]:
            btype = "vector<double>"
        elif btype in ["long double"]:
            btype = "vector<long double>"

    if block_membership is not None:
        bm = g.new_vertex_property(btype)
        if btype in ["object", "string"] or "vector" in btype:
            for v in g.vertices():
                if not degree_block:
                    bm[v] = block_membership[int(v)]
                else:
                    if g.is_directed():
                        bm[v] = (block_membership[int(v)], v.in_degree(),
                                 v.out_degree())
                    else:
                        bm[v] = (block_membership[int(v)], v.out_degree())
        else:
            try:
                bm.a = block_membership
            except ValueError:
                bm = g.new_vertex_property("object")
                for v in g.vertices():
                    bm[v] = block_membership[int(v)]
    else:
        bm = None

    if random:
        random_rewire(g, parallel_edges=parallel_edges,
                      self_loops=self_loops, verbose=verbose,
                      block_membership=bm, **kwargs)

    if bm is None:
        return g
    else:
        return g, bm


@_limit_args({"model": ["erdos", "correlated", "uncorrelated",
                        "probabilistic", "blockmodel",
                        "blockmodel-traditional"]})
def random_rewire(g, model="uncorrelated", n_iter=1, edge_sweep=True,
                  parallel_edges=False, self_loops=False, vertex_corr=None,
                  block_membership=None, alias=True, cache_probs=True,
                  persist=False, ret_fail=False, verbose=False):
    r"""

    Shuffle the graph in-place, following a variety of possible statistical
    models, chosen via the parameter ``model``.


    Parameters
    ----------
    g : :class:`~graph_tool.Graph`
        Graph to be shuffled. The graph will be modified.
    model : string (optional, default: ``"uncorrelated"``)
        The following statistical models can be chosen, which determine how the
        edges are rewired.

        ``erdos``
           The edges will be rewired entirely randomly, and the resulting graph
           will correspond to the Erdős–Rényi model.
        ``uncorrelated``
           The edges will be rewired randomly, but the degree sequence of the
           graph will remain unmodified.
        ``correlated``
           The edges will be rewired randomly, but both the degree sequence of
           the graph and the *vertex-vertex degree correlations* will remain
           unmodified.
        ``probabilistic``
           This is similar to the ``correlated`` option, but the vertex-vertex
           correlations are not kept unmodified, but instead are sampled from an
           arbitrary degree-based probabilistic model specified via the
           ``vertex_corr`` parameter.
        ``blockmodel``
          This is just like ``probabilistic``, but the values passed to the
          ``vertex_corr`` function will correspond to the block membership
          values specified by the ``block_membership`` parameter.
        ``blockmodel-traditional``
          This is just like ``blockmodel``, but the degree sequence *is not*
          preserved during rewiring.

    n_iter : int (optional, default: ``1``)
        Number of iterations. If ``edge_sweep == True``, each iteration
        corresponds to an entire "sweep" over all edges. Otherwise this
        corresponds to the total number of edges which are randomly chosen for a
        swap attempt (which may repeat).
    edge_sweep : bool (optional, default: ``True``)
        If ``True``, each iteration will perform an entire "sweep" over the
        edges, where each edge is visited once in random order, and a edge swap
        is attempted.
    parallel : bool (optional, default: ``False``)
        If ``True``, parallel edges are allowed.
    self_loops : bool (optional, default: ``False``)
        If ``True``, self-loops are allowed.
    vertex_corr : function (optional, default: ``None``)
        A function which gives the vertex-vertex correlation of the graph.

        If ``model == probabilistic`` it should be callable with two parameters:
        the (in, out)-degree pair of the source vertex an edge, and the
        (in,out)-degree pair of the target of the same edge (for undirected
        graphs, both parameters are single values). The function should return a
        number proportional to the probability of such an edge existing in the
        generated graph.

        If ``model == blockmodel`` or ``model == blockmodel-traditional``, the
        values passed to the function will be the block value of the respective
        vertices, as specified via the ``block_membership``. The function should
        also return a number proportional to the probability of such an edge
        existing in the generated graph.
    block_membership : :class:`~graph_tool.PropertyMap` (optional, default: ``None``)
        If supplied, the graph will be rewired to conform to a blockmodel
        ensemble. The value must be a vertex property map which defines the
        block of each vertex.
    alias : bool (optional, default: ``True``)
        If ``True``, and ``model`` is any of ``probabilistic``, ``blockmodel``,
        or ``blockmodel-traditional``, the alias method will be used to sample
        the candidate edges. In the case of ``blockmodel-traditional``, if
        ``parallel_edges == True`` and ``self_loops == True`` this makes the
        sampling of the edges direct (not rejection based), so that
        ``n_iter == 1`` is enough to get an uncorrelated sample.
    cache_probs : bool (optional, default: ``True``)
        If ``True``, the probabilities returned by the ``vertex_corr`` parameter
        will be cached internally. This is crucial for good performance, since
        in this case the supplied python function is called only a few times,
        and not at every attempted edge rewire move. However, in the case were
        the different parameter combinations to the probability function is very
        large, the memory and time requirements to keep the cache may not be
        worthwhile.
    persist : bool (optional, default: ``False``)
        If ``True``, an edge swap which is rejected will be attempted again
        until it succeeds. This may improve the quality of the shuffling for
        some probabilistic models, and should be sufficiently fast for sparse
        graphs, but otherwise it may result in many repeated attempts for
        certain corner-cases in which edges are difficult to swap.
    verbose : bool (optional, default: ``False``)
        If ``True``, verbose information is displayed.


    Returns
    -------
    rejection_count : int
        Number of rejected edge moves (due to parallel edges or self-loops, or
        the probabilistic model used).

    See Also
    --------
    random_graph: random graph generation

    Notes
    -----
    This algorithm iterates through all the edges in the network and tries to
    swap its target or source with the target or source of another edge. The
    selected canditate swaps are chosen according to the ``model`` parameter.

    .. note::

        If ``parallel_edges = False``, parallel edges are not placed during
        rewiring. In this case, the returned graph will be a uncorrelated sample
        from the desired ensemble only if ``n_iter`` is sufficiently large. The
        algorithm implements an efficient Markov chain based on edge swaps, with
        a mixing time which depends on the degree distribution and correlations
        desired. If degree probabilistic correlations are provided, the mixing
        time tends to be larger.

        If ``model`` is either "probabilistic" or "blockmodel", the Markov chain
        still needs to be mixed, even if parallel edges and self-loops are
        allowed. In this case the Markov chain is implemented using the
        Metropolis-Hastings [metropolis-equations-1953]_
        [hastings-monte-carlo-1970]_ acceptance/rejection algorithm. It will
        eventually converge to the desired probabilities for sufficiently large
        values of ``n_iter``.


    Each edge is tentatively swapped once per iteration, so the overall
    complexity is :math:`O(V + E \times \text{n-iter})`. If ``edge_sweep ==
    False``, the complexity becomes :math:`O(V + E + \text{n-iter})`.

    Examples
    --------

    Some small graphs for visualization.

    .. testcode::
       :hide:

       from numpy.random import random, seed
       from pylab import *
       seed(43)
       gt.seed_rng(42)

    >>> g, pos = gt.triangulation(random((1000,2)))
    >>> pos = gt.arf_layout(g)
    >>> gt.graph_draw(g, pos=pos, output="rewire_orig.pdf", output_size=(300, 300))
    <...>

    .. testcode::
       :hide:

       gt.graph_draw(g, pos=pos, output="rewire_orig.png", output_size=(300, 300))

    >>> gt.random_rewire(g, "correlated")
    189
    >>> pos = gt.arf_layout(g)
    >>> gt.graph_draw(g, pos=pos, output="rewire_corr.pdf", output_size=(300, 300))
    <...>

    .. testcode::
       :hide:

       gt.graph_draw(g, pos=pos, output="rewire_corr.png", output_size=(300, 300))

    >>> gt.random_rewire(g)
    197
    >>> pos = gt.arf_layout(g)
    >>> gt.graph_draw(g, pos=pos, output="rewire_uncorr.pdf", output_size=(300, 300))
    <...>

    .. testcode::
       :hide:

       gt.graph_draw(g, pos=pos, output="rewire_uncorr.png", output_size=(300, 300))

    >>> gt.random_rewire(g, "erdos")
    26
    >>> pos = gt.arf_layout(g)
    >>> gt.graph_draw(g, pos=pos, output="rewire_erdos.pdf", output_size=(300, 300))
    <...>

    .. testcode::
       :hide:

       gt.graph_draw(g, pos=pos, output="rewire_erdos.png", output_size=(300, 300))

    Some `ridiculograms <http://www.youtube.com/watch?v=YS-asmU3p_4>`_ :

    .. image:: rewire_orig.*
    .. image:: rewire_corr.*
    .. image:: rewire_uncorr.*
    .. image:: rewire_erdos.*

    **From left to right**: Original graph; Shuffled graph, with degree correlations;
    Shuffled graph, without degree correlations; Shuffled graph, with random degrees.

    We can try with larger graphs to get better statistics, as follows.

    >>> figure()
    <...>
    >>> g = gt.random_graph(30000, lambda: sample_k(20), model="probabilistic",
    ...                     vertex_corr=lambda i, j: exp(abs(i-j)), directed=False,
    ...                     n_iter=100)
    >>> corr = gt.avg_neighbour_corr(g, "out", "out")
    >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-", label="Original")
    <...>
    >>> gt.random_rewire(g, "correlated")
    206
    >>> corr = gt.avg_neighbour_corr(g, "out", "out")
    >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="*", label="Correlated")
    <...>
    >>> gt.random_rewire(g)
    109
    >>> corr = gt.avg_neighbour_corr(g, "out", "out")
    >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-", label="Uncorrelated")
    <...>
    >>> gt.random_rewire(g, "erdos")
    13
    >>> corr = gt.avg_neighbour_corr(g, "out", "out")
    >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-", label=r"Erd\H{o}s")
    <...>
    >>> xlabel("$k$")
    <...>
    >>> ylabel(r"$\left<k_{nn}\right>$")
    <...>
    >>> legend(loc="best")
    <...>
    >>> savefig("shuffled-stats.pdf")

    .. testcode::
       :hide:

       savefig("shuffled-stats.png")


    .. figure:: shuffled-stats.*
        :align: center

        Average degree correlations for the different shuffled and non-shuffled
        graphs. The shuffled graph with correlations displays exactly the same
        correlation as the original graph.

    Now let's do it for a directed graph. See
    :func:`~graph_tool.generation.random_graph` for more details.

    >>> p = scipy.stats.poisson
    >>> g = gt.random_graph(20000, lambda: (sample_k(19), sample_k(19)),
    ...                     model="probabilistic",
    ...                     vertex_corr=lambda a, b: (p.pmf(a[0], b[1]) * p.pmf(a[1], 20 - b[0])),
    ...                     n_iter=100)
    >>> figure()
    <...>
    >>> axes([0.1,0.15,0.6,0.8])
    <...>
    >>> corr = gt.avg_neighbour_corr(g, "in", "out")
    >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-",
    ...          label=r"$\left<\text{o}\right>$ vs i")
    <...>
    >>> corr = gt.avg_neighbour_corr(g, "out", "in")
    >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-",
    ...          label=r"$\left<\text{i}\right>$ vs o")
    <...>
    >>> gt.random_rewire(g, "correlated")
    4323
    >>> corr = gt.avg_neighbour_corr(g, "in", "out")
    >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-",
    ...          label=r"$\left<\text{o}\right>$ vs i, corr.")
    <...>
    >>> corr = gt.avg_neighbour_corr(g, "out", "in")
    >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-",
    ...          label=r"$\left<\text{i}\right>$ vs o, corr.")
    <...>
    >>> gt.random_rewire(g, "uncorrelated")
    153
    >>> corr = gt.avg_neighbour_corr(g, "in", "out")
    >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-",
    ...          label=r"$\left<\text{o}\right>$ vs i, uncorr.")
    <...>
    >>> corr = gt.avg_neighbour_corr(g, "out", "in")
    >>> errorbar(corr[2][:-1], corr[0], yerr=corr[1], fmt="o-",
    ...          label=r"$\left<\text{i}\right>$ vs o, uncorr.")
    <...>
    >>> legend(bbox_to_anchor=(1.01, 0.5), loc="center left", borderaxespad=0.)
    <...>
    >>> xlabel("Source degree")
    <...>
    >>> ylabel("Average target degree")
    <...>
    >>> savefig("shuffled-deg-corr-dir.pdf")

    .. testcode::
       :hide:

       savefig("shuffled-deg-corr-dir.png")

    .. figure:: shuffled-deg-corr-dir.*
        :align: center

        Average degree correlations for the different shuffled and non-shuffled
        directed graphs. The shuffled graph with correlations displays exactly
        the same correlation as the original graph.

    References
    ----------
    .. [metropolis-equations-1953]  Metropolis, N.; Rosenbluth, A.W.;
       Rosenbluth, M.N.; Teller, A.H.; Teller, E. "Equations of State
       Calculations by Fast Computing Machines". Journal of Chemical Physics 21
       (6): 1087-1092 (1953). :doi:`10.1063/1.1699114`
    .. [hastings-monte-carlo-1970] Hastings, W.K. "Monte Carlo Sampling Methods
       Using Markov Chains and Their Applications". Biometrika 57 (1): 97-109 (1970).
       :doi:`10.1093/biomet/57.1.97`
    .. [holland-stochastic-1983] Paul W. Holland, Kathryn Blackmond Laskey, and
       Samuel Leinhardt, "Stochastic blockmodels: First steps," Social Networks
       5, no. 2: 109-13 (1983) :doi:`10.1016/0378-8733(83)90021-7`
    .. [karrer-stochastic-2011] Brian Karrer and M. E. J. Newman, "Stochastic
       blockmodels and community structure in networks," Physical Review E 83,
       no. 1: 016107 (2011) :doi:`10.1103/PhysRevE.83.016107` :arxiv:`1008.3926`

    """
    if not parallel_edges:
        p = label_parallel_edges(g)
        if p.a.max() != 0:
            raise ValueError("Parallel edge detected. Can't rewire " +
                             "graph without parallel edges if it " +
                             "already contains parallel edges!")
    if not self_loops:
        l = label_self_loops(g)
        if l.a.max() != 0:
            raise ValueError("Self-loop detected. Can't rewire graph " +
                             "without self-loops if it already contains" +
                             " self-loops!")

    if (vertex_corr is not None and not g.is_directed()) and "blockmodel" not in model:
        corr = lambda i, j: vertex_corr(i[1], j[1])
    else:
        corr = vertex_corr

    if model not in ["probabilistic", "blockmodel", "blockmodel-traditional"]:
        g = GraphView(g, reversed=False)

    traditional = False
    if model == "blockmodel-traditional":
        model = "blockmodel"
        traditional = True
    pcount = libgraph_tool_generation.random_rewire(g._Graph__graph, model,
                                                    n_iter, not edge_sweep,
                                                    self_loops, parallel_edges,
                                                    alias, traditional, persist,
                                                    corr, _prop("v", g, block_membership),
                                                    cache_probs,
                                                    _get_rng(), verbose)
    return pcount


def predecessor_tree(g, pred_map):
    """Return a graph from a list of predecessors given by the ``pred_map`` vertex property."""

    _check_prop_scalar(pred_map, "pred_map")
    pg = Graph()
    libgraph_tool_generation.predecessor_graph(g._Graph__graph,
                                               pg._Graph__graph,
                                               _prop("v", g, pred_map))
    return pg


def line_graph(g):
    """Return the line graph of the given graph `g`.

    Notes
    -----
    Given an undirected graph G, its line graph L(G) is a graph such that

        * each vertex of L(G) represents an edge of G; and
        * two vertices of L(G) are adjacent if and only if their corresponding
          edges share a common endpoint ("are adjacent") in G.

    For a directed graph, the second criterion becomes:

       * Two vertices representing directed edges from u to v and from w to x in
         G are connected by an edge from uv to wx in the line digraph when v =
         w.


    Examples
    --------

    >>> g = gt.collection.data["lesmis"]
    >>> lg, vmap = gt.line_graph(g)
    >>> gt.graph_draw(g, pos=g.vp["pos"], output="lesmis.pdf")
    <...>
    >>> pos = gt.graph_draw(lg, output="lesmis-lg.pdf")

    .. testcode::
       :hide:

       gt.graph_draw(g, pos=g.vp["pos"], output="lesmis.png")
       pos = gt.graph_draw(lg, pos=pos, output="lesmis-lg.png")


    .. figure:: lesmis.png
       :align: left

       Coappearances of characters in Victor Hugo's novel "Les Miserables".

    .. figure:: lesmis-lg.png
       :align: right

       Line graph of the coappearance network on the left.

    References
    ----------
    .. [line-wiki] http://en.wikipedia.org/wiki/Line_graph
    """
    lg = Graph(directed=g.is_directed())

    vertex_map = lg.new_vertex_property("int64_t")

    libgraph_tool_generation.line_graph(g._Graph__graph,
                                        lg._Graph__graph,
                                        _prop("v", lg, vertex_map))
    return lg, vertex_map


def graph_union(g1, g2, intersection=None, props=None, include=False):
    """Return the union of graphs g1 and g2, composed of all edges and vertices
    of g1 and g2, without overlap.

    Parameters
    ----------
    g1 : :class:`~graph_tool.Graph`
       First graph in the union.
    g2 : :class:`~graph_tool.Graph`
       Second graph in the union.
    intersection : :class:`~graph_tool.PropertyMap` (optional, default: ``None``)
       Vertex property map owned by `g1` which maps each of each of its vertices
       to vertex indexes belonging to `g2`. Negative values mean no mapping
       exists, and thus both vertices in `g1` and `g2` will be present in the
       union graph.
    props : list of tuples of :class:`~graph_tool.PropertyMap` (optional, default: ``[]``)
       Each element in this list must be a tuple of two PropertyMap objects. The
       first element must be a property of `g1`, and the second of `g2`. The
       values of the property maps are propagated into the union graph, and
       returned.
    include : bool (optional, default: ``False``)
       If true, graph `g2` is inserted into `g1` which is modified. If false, a
       new graph is created, and both graphs remain unmodified.

    Returns
    -------
    ug : :class:`~graph_tool.Graph`
        The union graph
    props : list of :class:`~graph_tool.PropertyMap` objects
        List of propagated properties.  This is only returned if `props` is not
        empty.

    Examples
    --------

    .. testcode::
       :hide:

       from numpy.random import random, seed
       from pylab import *
       seed(42)
       gt.seed_rng(42)

    >>> g = gt.triangulation(random((300,2)))[0]
    >>> ug = gt.graph_union(g, g)
    >>> uug = gt.graph_union(g, ug)
    >>> pos = gt.sfdp_layout(g)
    >>> gt.graph_draw(g, pos=pos, output_size=(300,300), output="graph_original.pdf")
    <...>

    .. testcode::
       :hide:

       gt.graph_draw(g, pos=pos, output_size=(300,300), output="graph_original.png")

    >>> pos = gt.sfdp_layout(ug)
    >>> gt.graph_draw(ug, pos=pos, output_size=(300,300), output="graph_union.pdf")
    <...>

    .. testcode::
       :hide:

       gt.graph_draw(ug, pos=pos, output_size=(300,300), output="graph_union.png")

    >>> pos = gt.sfdp_layout(uug)
    >>> gt.graph_draw(uug, pos=pos, output_size=(300,300), output="graph_union2.pdf")
    <...>

    .. testcode::
       :hide:

       gt.graph_draw(uug, pos=pos, output_size=(300,300), output="graph_union2.png")


    .. image:: graph_original.*
    .. image:: graph_union.*
    .. image:: graph_union2.*

    """
    if props == None:
        props = []
    if not include:
        g1 = Graph(g1)
    if intersection is None:
        intersection = g1.new_vertex_property("int32_t")
        intersection.a = 0
    else:
        intersection = intersection.copy("int32_t")
        intersection.a[intersection.a >= 0] += 1
        intersection.a[intersection.a < 0] = 0

    g1.stash_filter(directed=True)
    g1.set_directed(True)
    g2.stash_filter(directed=True)
    g2.set_directed(True)
    n_props = []

    try:
        vmap, emap = libgraph_tool_generation.graph_union(g1._Graph__graph,
                                                          g2._Graph__graph,
                                                          _prop("v", g1,
                                                                intersection))
        for p1, p2 in props:
            if not include:
                p1 = g1.copy_property(p1)
            if p2.value_type() != p1.value_type():
                p2 = g2.copy_property(p2, value_type=p1.value_type())
            if p1.key_type() == 'v':
                libgraph_tool_generation.\
                      vertex_property_union(g1._Graph__graph, g2._Graph__graph,
                                            vmap, emap,
                                            _prop(p1.key_type(), g1, p1),
                                            _prop(p2.key_type(), g2, p2))
            else:
                libgraph_tool_generation.\
                      edge_property_union(g1._Graph__graph, g2._Graph__graph,
                                          vmap, emap,
                                          _prop(p1.key_type(), g1, p1),
                                          _prop(p2.key_type(), g2, p2))
            n_props.append(p1)
    finally:
        g1.pop_filter(directed=True)
        g2.pop_filter(directed=True)

    if len(n_props) > 0:
        return g1, n_props
    else:
        return g1


@_limit_args({"type": ["simple", "delaunay"]})
def triangulation(points, type="simple", periodic=False):
    r"""
    Generate a 2D or 3D triangulation graph from a given point set.

    Parameters
    ----------
    points : :class:`~numpy.ndarray`
        Point set for the triangulation. It may be either a N x d array, where N
        is the number of points, and d is the space dimension (either 2 or 3).
    type : string (optional, default: ``'simple'``)
        Type of triangulation. May be either 'simple' or 'delaunay'.
    periodic : bool (optional, default: ``False``)
        If ``True``, periodic boundary conditions will be used. This is
        parameter is valid only for type="delaunay", and is otherwise ignored.